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arXiv:2505.09814cs.DS

$XX^{t}$ Can Be Faster

Dmitry Rybin, Yushun Zhang, Zhi-Quan Luo

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Abstract

We present RXTX, a new algorithm for computing the product of matrix by its transpose $XX^{t}$ for $X\in \mathbb{R}^{n\times m}$. RXTX uses $5\%$ fewer multiplications and $5\%$ fewer operations (additions and multiplications) than State-of-the-Art algorithms. Note that the accelerations not only holds asymptotically for large matrices with $n \rightarrow \infty$, but also for small matrices including $n = 4$. The algorithm was discovered by combining Machine Learning-based search methods with Combinatorial Optimization.

arXiv comments
improved presentation
Journal reference
DOI
Subjects
cs.DSData Structures and Algorithms cs.AIArtificial Intelligence cs.LGMachine Learning cs.SCSymbolic Computation

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Jul 2026 to Jul 2026. Found in 1 HN submission from May 2025 to May 2025. Open submission discussions.

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    • If you’re interested, I found https://arxiv.org/abs/2505.09814v1 to beat Strassen for medium-sized and larger covariance matrices. YMMV of course. Takes a little adjustment for XX^H but it’s not so bad.

      TimorousBestieView on HNParent context

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